An efficient moving object detection algorithm based on improved GMM and cropped frame technique
Jinfu Yang, Wanlu Yang, Mingai Li · 2012
Gaussian mixture model (GMM) is an effective way to extract moving object from a video sequence. However, the conventional mixture Gaussian method suffers from slow convergence. In this paper, a novel approach which combines Gaussian mixture model (GMM), three-frame-difference and cropped frame technique is proposed to detect moving object. Firstly, gray correlation based three-frame-difference method is adopted to make sure the motion region. Then, a cropped frame technique is presented to clip the motion region of an image. And the background model is estimated only on the motion region, which can dramatically reduce computational load. At the same time, we propose a new initialization strategy based on dynamic grid and density estimation for EM algorithm to reduce the influence on initial values. Extensive experimental results demonstrate that our approach can obtain satisfying performances for practical application.